1. Field of the Invention
The present invention relates to a machine learning apparatus, a motor control apparatus, and a machine learning method, and in particular to a machine learning apparatus for learning optimization of a current gain parameter in electrical machine control, a motor control apparatus equipped with such a machine learning apparatus, and a machine learning method for the same.
2. Description of the Related Art
PI (proportional-integral) control is a current control system known for use in variable speed control of a motor using an inverter; in PI control, control is performed based on two control terms, a proportional gain function and an integral gain function (for example, refer to Japanese Unexamined Patent Publication No. 2000-184785, hereinafter cited as “patent document 1”).
The current control gain adjustment method disclosed in patent document 1 is a method for adjusting the proportional gain in PI control; more specifically, the method measures the delay phase or delay time of a current detection signal waveform by comparing the current detection signal waveform with a single-phase AC current command signal, determines whether or not the detected delay is larger than a predetermined target delay phase or delay time, and makes an adjustment so as to increase the proportional gain when the detected delay is larger and decrease the proportional gain when the detected delay is smaller.
In the conventional art, a proper current gain parameter is set by calculating it from a physical constant or the like. Further, the conventional art has involved a certain degree of discrepancy (error) between the optimum value and the calculated value because, for example, the inductance varies due to the current. As a result, a human operator has had to make a fine adjustment by observing a step response or frequency response while varying the parameter.
It is therefore hard to say that such a conventional art method is optimum, and also the conventional art has had the problem that the adjustment takes time and trouble (labor). Furthermore, since the motor physical constant differs from one motor to another, the truly optimum parameter also differs from one motor to another, and hence the problem that optimizing the parameter for each individual motor takes even more time and labor and is therefore not realistic.